Leveling the Playing Field: A New Proposed Method to Address Relative Age- and Maturity-Related Bias in Soccer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite various solutions proposed to solve the relative age effect (RAE), it is still a major problem confounding talent identification and selection processes. In the first phase, we sampled 302 under 7–21 academy soccer players from two Belgian professional soccer clubs to explore the potential of a new approach to solve the inequalities resulting from relative age- and maturity-related bias. This approach allocates players into four discrete quartile groups based on the midway point of their chronological and estimated developmental (ED) birth dates (calculated using the growth curves for stature of Belgian youth). With the use of chi square analyses, a RAE was found ( p < 0.01) for the overall sample (Q1 = 41.4% vs. Q4 = 14.9%) that completely disappeared after reallocation (Q1 = 26.5%; Q2 = 21.9%; Q3 = 27.5%; Q4 = 24.2%). According to the new allocation method, the stature difference was reduced, on average, by 11.6 cm (from 24.0 ± 9.9 to 12.4 ± 3.4 cm, d = 1.57). Body mass difference between the two methods was 1.9 kg (20.1 ± 11.3–18.2 ± 13.1 kg, respectively, d = 0.15). The new method created a maximum chronological age difference of 1.9 vs. 0.8 years for the current method. With the use of this method, 47% of the players would be reallocated. Twenty-three percent would be moved up one age category, and 21% would be moved down. In the second phase, we also examined 80 UK academy soccer players to explore if reallocating players reduces the within-playing group variation of somatic and physical fitness characteristics. The percentage coefficient of variation (%CV) was reduced (0.2–10.1%) in 15 out of 20 metrics across U11–U16 age categories, with the U13 age category demonstrating the largest reductions (0.9–10.1%) in CV. The U12 and U13 age categories and associated reallocation groupings showed trivial to small (ES = 0.0–0.5) between-method differences and trivial to moderate (ES = 0.0–1.1) differences within the U14–U16 age categories. A reduction in RAE may lead to fewer dropouts and thus a larger player pool, which benefits, in turn, talent identification, selection, and development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it